Using machine learning to predict obesity in high school students

Zheng, Zeyu; Ruggiero, Karen

doi:10.1109/bibm.2017.8217988

Cited by 33 publications

(20 citation statements)

References 7 publications

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“…The former models use traditional statistical techniques, mainly logistic regression, [ 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] although there are cases using linear regression [ 40 ], quantile regression [ 38 ], and ordinal logistic regression. [ 47 ] The ML models use a wide variety of ML methods: ANN [ 56 , 57 , 58 , 67 , 68 , 70 , 75 ], SVM [ 58 , 66 , 67 ], DT [ 58 , 64 , 65 , 67 , 68 , 69 , 70 , 73 ], NB [ 58 , 60 , 61 , 62 , 66 , 67 ], BN [ 58 , 65 , 67 , 76 ], LASSO [ 72 , 74 ], kNN [ 70 ], RF [ 59 , 65 , 68 , 72 ], GBM [ 72 , 77 ], and DL (RNN […”

Section: Discussionmentioning

confidence: 99%

“…In general, when in the same work logistic/linear regression is compared with ML models when fitting the same dataset [ 58 , 64 , 68 , 70 , 72 , 75 ], the latter give better results than the former in terms of prediction performance. This confirms that ML techniques are able to yield better predictions, not just by fitting better the training set but also through giving better results in internal and/or external validations.…”

Section: Discussionmentioning

confidence: 99%

“…The last paper in 2017 is that of Zheng and Ruggiero [ 70 ]. They used a dataset comprising a cohort of >5000 high-school (14–18 years) students in the USA.…”

Section: Machine Learning Models To Predict Childhood/adolescent Omentioning

confidence: 99%

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Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Colmenarejo

2020

Nutrients

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The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Colmenarejo

2020

Nutrients

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“…Zheng et al [24] used binary logistic regression, improved decision tree (IDT), weighted k-nearest neighbor (KNN), and artificial neural network (ANN) on nine health-related behaviors from the 2015 Youth Risk Behavior Surveillance System (YRBSS) for the state of Tennessee in their study to predict obesity in high school students by focusing on both risk and protective factors. The result showed that the IDT model achieved an 80.23% accuracy and 90.74% specificity, the weighted KNN model achieved an 88.82% accuracy and 93.44% specificity, and the ANN model achieved an 84.22% accuracy and 99.46% specificity in the classification problem.…”

Section: Related Workmentioning

confidence: 99%

“…During data cleaning, we removed data that were incomplete, beyond the age >20 and <60, and features such as pregnancy, having children/number of children. Data processing incorporates three steps, as stated below [24,25,30,31]:…”

Section: Data Processingmentioning

confidence: 99%

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Chatterjee

Gerdes

Martínez

2020

Sensors

155

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Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and “obesity/overweight” is one of the consequences. “Obesity and overweight” are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The “World Health Organization” (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as “Center for Disease Control and Prevention (CDC)” and “National Institute for Clinical Excellence (NICE)” guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in “Kaggle” and “University of California, Irvine (UCI) database”, and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting “obesity and overweight” as a study case in the future? (c) why have we used the existing “Kaggle” and “UCI” datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?

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